Traffic corridor signal cooperative control method based on single-agent reinforcement learning

By using a single-agent reinforcement learning method, real-time traffic data is acquired and action vectors are generated. The phase division of signalized intersections is adjusted synchronously, which solves the efficiency and adaptability problems of signal coordinated control in multi-intersection traffic corridors, and achieves efficient congestion relief and improved traffic efficiency.

CN121661849AActive Publication Date: 2026-03-13SHENZHEN TECH UNIV
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Patent Information

Application Number
CN202610154817.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-04
Publication Date
2026-03-13
Estimated Expiration
2046-02-04

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve efficient and adaptive signal coordination control in multi-intersection traffic corridors, especially when faced with sudden congestion and tidal traffic flow, where traditional methods are ineffective. Furthermore, multi-agent systems suffer from environmental instability, training instability, and high complexity.

Method used

A traffic corridor signal cooperative control method based on single-agent reinforcement learning is adopted. By acquiring real-time traffic state data and the current signal control scheme, a state vector is constructed, and an action vector is generated using a pre-trained single-agent reinforcement learning model. The phase division of the signalized intersection is adjusted synchronously to generate a new signal control scheme.

Benefits of technology

It achieves centralized and coordinated control of all signalized intersections within the traffic corridor, avoiding the complexity and training instability of multi-agent systems, improving overall traffic efficiency and congestion mitigation capabilities, and enabling rapid response to real-time changes in traffic demand.

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Abstract

The invention provides a traffic corridor signal cooperative control method based on single agent reinforcement learning, and relates to the technical field of traffic management and control, and the control method comprises the steps: obtaining the real-time traffic state data of a traffic corridor comprising a plurality of signal intersections; acquiring a current signal control scheme of the traffic corridor; constructing a state vector according to the real-time traffic state data and the current signal control scheme; inputting the state vector into a pre-trained single-agent reinforcement learning model to obtain a corresponding action vector; based on the action vectors, phase division of all the signal intersections is synchronously adjusted, and a new signal control scheme is generated; according to the invention, signal timing of all signal intersections in a traffic corridor is cooperatively controlled by adopting a centralized single-agent architecture, so that the problems of system complexity and training instability caused by local observation, distributed decision and communication coordination among agents in a multi-agent scheme are fundamentally avoided.
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